{"doi":"10.1101/2024.02.22.580842","title":"Interpretable deep learning reveals the sequence rules of Hippo signaling","abstract":"Summary The response to signaling pathways is highly context-specific, and identifying the transcription factors and mechanisms that are responsible is very challenging. Using the Hippo pathway in mouse trophoblast stem cells as a model, we show here that this information is encoded in cis -regulatory sequences and can be learned from high-resolution binding data of signaling transcription factors. Using interpretable deep learning, we show that the binding levels of TEAD4 and YAP1 are enhanced in a distance-dependent manner by cell type-specific transcription factors, including TFAP2C. We also discovered that strictly spaced Tead double motifs are widespread highly active canonical response elements that mediate cooperativity by promoting labile TEAD4 protein-protein interactions on DNA. These syntax rules and mechanisms apply genome-wide and allow us to predict how small sequence changes alter the activity of enhancers in vivo . This illustrates the power of interpretable deep learning to decode canonical and cell type-specific sequence rules of signaling pathways. Graphical abstract","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":490124,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9375,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":615218,"name":"Charles E. McAnany","orcid":"0000-0002-4882-7192","position":1,"is_corresponding":false},{"id":615215,"name":"Melanie Weilert","orcid":"0000-0001-8683-4580","position":2,"is_corresponding":false},{"id":16250,"name":"Mary Cathleen McKinney","orcid":"0000-0002-6819-1419","position":3,"is_corresponding":false},{"id":615216,"name":"Sabrina Krueger","orcid":"0000-0001-6581-1590","position":4,"is_corresponding":false},{"id":23592,"name":"Julia Zeitlinger","orcid":"0000-0002-5172-3335","position":5,"is_corresponding":false},{"id":615217,"name":"Khyati Dalal","orcid":"0000-0002-1867-5079","position":0,"is_corresponding":true}],"reference_count":161,"raw_metadata":null,"created_at":"2026-07-19T02:08:32.775003Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}